Biology subjects

Mondal, Y.

Publications and source records attributed to Mondal, Y..

4 recordsLinked to original sources

bioRxiv2025

Persistent Adaptation through Dual-Timescale Regulation of Ion Channel Properties

Neurons are terminally differentiated cells that adapt to maintain stable function over years, despite encountering a wide range of environmental perturbations. Some adaptations are transient, fading once the perturbation ends. Others are persistent, continuing to influence a neurons responses to future challenges even after baseline conditions are restored. These persistent adaptations are especially intriguing because some remain undetectable under normal conditions--only becoming apparent upon re-exposure to a perturbation. Among the many mechanisms that may contribute to persistent adaptation, we investigate one based on the regulation of intrinsic currents. Using a computational model of activity-dependent homeostasis, we show that slow changes in channel density can encode the influence of past experience and shape future responses while rapid shifts in ion channel voltage-dependence provide immediate compensation during perturbations. Together, these dual processes tune a neurons intrinsic excitability, enabling persistent adaptation. SIGNIFICANCE STATEMENTSome neurons display a remarkable property: after adapting to environmental challenges, they return to baseline activity that looks unchanged, even though underlying modifications were made. These modifications may persist, shaping how the neuron responds to future perturbations. We term this phenomenon persistent adaptation. Although the underlying mechanisms of persistent adaptations may be diverse, here we demonstrate one possible route: homeostatic regulation of intrinsic excitability. Slow changes in channel density can store a trace of past perturbations, while rapid shifts in channel voltage-dependence can enable immediate recovery during re-exposure.

neuroscience↗

bioRxiv2025

Activity-Dependent Changes in Ion Channel Voltage-Dependence Influence the Activity Patterns Targeted by Neurons

Neurons can maintain stable activity profiles over their lifetimes despite ion channel turnover over minutes to weeks. Neuronal activity is also influenced by regulating the voltage-dependence of ion channels. How do these two forms of plasticity work together to maintain a stable activity profile? We augment a classical model of activity-dependent ion channel density regulation with a mechanism that adjusts channel voltage-dependence based on activity. These findings reveal that the timescale of these mechanisms shape the specific electrical activity patterns that achieve a target activity profile. Moreover, alterations in channel voltage-dependence can impact a neurons ability to recover from perturbations. These results highlight a potentially distinct role for activity-dependent regulation of channel voltage-dependence in maintaining stable neuronal activity profiles.

neuroscience↗

bioRxiv2023

Postsynaptic frequency filters shaped by the interplay of synaptic short-term plasticity and cellular time scales

Neuronal frequency filters can be thought of as constituent building blocks underlying the ability of neuronal systems to process information, generate rhythms and perform computations. How neuronal filters are generated by the concerted activity of a multiplicity of processes (e.g., electric circuit, history-dependent) and interacting time scales within and across levels of neuronal network organization is poorly understood. In this paper, we use mathematical modeling, numerical simulations and analytical calculations of the postsynaptic response to presynaptic spike trains to address this issue in a basic feedforward network motif in the presence of synaptic short-term plasticity (STP, depression and facilitation). The network motif consists of a presynaptic spike-train, a postsynaptic passive cell, and an excitatory (AMPA) chemical synapse. The dynamics of each network component are controlled by one or more time scales. We explain the mechanisms by which the participating time scales shape the neuronal filters at the (i) synaptic update level (the target of the synaptic variable in response to presynaptic spikes), which is shaped by STP, (ii) the synaptic level, and (iii) the postsynaptic membrane potential (PSP) level. We focus on three metrics that gives rise to three types of profiles (curves of the corresponding metrics as a function of the spike-train input frequency or firing rate): (i) peak profiles, (ii) peak-to-trough amplitude profiles, and (iii) phase profiles. The effects of STP are present at the synaptic update level and are communicated to the synaptic level where they interact with the synaptic time scales. The PSP filters result from the interaction between these variables and time scales and the biophysical properties and time scales of the postsynaptic cell. Band-pass filters (BPFs) result from a combination of low-pass filters (LPFs) and high-pass filters (HPFs) operating at the same or different levels of organization. PSP BPFs can be inherited from the synaptic level (STP-mediated BPFs) or they can be generated across levels of organization due to the interaction between (i) a synaptic LPF and the PSP summation-mediated HPF (PSP peaks), and (ii) a synaptic HPF and the PSP summation-mediated LPF (PSP amplitude). These types of BPFs persist in response to more realistic presynaptic spike trains: jittered (randomly perturbed) periodic spike trains and Poisson-distributed spike trains. The response variability is frequency-dependent and is controlled by STP in a non-monotonic frequency manner. The results and and lessons learned from the investigation of this basic network motif are a necessary step for the construction of a framework to analyze the mechanisms of generation of neuronal filters in networks with more complex architectures and a variety of interacting cellular, synaptic and plasticity time scales.

neuroscience↗

bioRxiv2021

Temporal filters in response to presynaptic spike trains: Interplay of cellular, synaptic and short-term plasticity time scales

Temporal filters, the ability of postsynaptic neurons to preferentially select certain presynaptic input patterns over others, have been shown to be associated with the notion of information filtering and coding of sensory inputs. Short-term plasticity (depression and facilitation; STP) has been proposed to be an important player in the generation of temporal filters. We carry out a systematic modeling, analysis and computational study to understand how characteristic postsynaptic (low-, high- and band-pass) temporal filters are generated in response to periodic presynaptic spike trains in the presence STP. We investi-gate how the dynamic properties of these filters depend on the interplay of a hierarchy of processes, including the arrival of the presynaptic spikes, the activation of STP, its effect on the excitatory synaptic connection efficacy, and the response of the postsynaptic cell. These mechanisms involve the inter-play of a collection of time scales that operate at the single-event level (roughly, during each presynaptic interspike-interval) and control the long-term development of the temporal filters over multiple presynaptic events. These time scales are generated at the levels of the presynaptic cell (captured by the presynaptic interspike-intervals), short-term depression and facilitation, synaptic dynamics and the post-synaptic cellular currents. We develop mathematical tools to link the single-event time scales with the time scales governing the long-term dynamics of the resulting temporal filters for a relatively simple model where depression and facilitation interact at the level of the synaptic efficacy change. We extend our results and tools to account for more complex models. These include multiple STP time scales and non-periodic presynaptic inputs. The results and ideas we develop have implications for the understanding of the generation of temporal filters in complex networks for which the simple feedforward network we investigate here is a building block.

neuroscience↗